L07 Layers

Data Visualization (STAT 302)

Author

Sherry Chen

Datasets

We’ll be using the tech_stocks.rda dataset which is already in the /data subdirectory in our data_vis_labs project.

We have a new dataset, NU_admission_data.csv, which will need to be downloaded and added to our /data subdirectory.

We will also be using the mpg dataset which comes packaged with ggplot2 — use ?ggplot2::mpg to access its codebook.

Code 
# load package(s)
library(tidyverse)
library(ggplot2)
library(patchwork)
library(readr)
library(janitor)
library(skimr)
# load datasets
load("data/tech_stocks.rda")

admin_data <- read_csv("data/NU_admission_data.csv") |> 
  janitor::clean_names()

Exercise 1

Using mpg and the class_dat dataset created below, recreate the following graphic as precisely as possible in two different ways.

Hints:

  • Transparency is 0.6
  • Horizontal spread is 0.1
  • Larger points are 5
  • Larger points are “red”
Code 
# additional dataset for plot
class_dat <- mpg |>
  group_by(class) |>
  summarise(
    n = n(),
    mean_hwy = mean(hwy),
    label = str_c("n = ", n, sep = "")
  )

Plot 1 – using mean_hwy

Solution
Code 
#load into session
data("mpg")



#scatterplot
ggplot(data = mpg, mapping = aes(class, hwy)) +
  geom_jitter(width = 0.1) +
  geom_point(
    data = class_dat, 
    mapping = aes(y = mean_hwy),
    color = "red",
    alpha = 0.6,
    size = 5) + 
  geom_text(
    data = class_dat,
    mapping = aes(y = 10, label = label)
  )

Code 
  theme_minimal() +
  labs(
    x = "Vehicle class",
    y = "Highway miles per gallon"
  )
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  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_line" "element"
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  [list output truncated]
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi TRUE
 - attr(*, "validate")= logi TRUE

Plot 2 – not using mean_hwy

Solution
Code 
#scatterplot
ggplot(data = mpg, mapping = aes(class, hwy)) +
  geom_jitter(width = 0.1) +
  geom_point(
    stat = "summary", 
    fun = "mean",
    color = "red",
    alpha = 0.6,
    size = 5) + 
  geom_text(
    data = class_dat,
    mapping = aes(y = 10, label = label)
  )

Code 
  theme_minimal() +
  labs(
    x = "Vehicle class",
    y = "Highway miles per gallon"
  )
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  ..$ arrow        : logi FALSE
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_line" "element"
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  ..$ fill         : chr "white"
  ..$ colour       : chr "black"
  ..$ linewidth    : num 0.5
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  ..$ inherit.blank: logi TRUE
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  ..$ vjust        : NULL
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ legend.title.position           : NULL
 $ legend.position                 : chr "right"
 $ legend.position.inside          : NULL
 $ legend.direction                : NULL
 $ legend.byrow                    : NULL
 $ legend.justification            : chr "center"
 $ legend.justification.top        : NULL
 $ legend.justification.bottom     : NULL
 $ legend.justification.left       : NULL
 $ legend.justification.right      : NULL
 $ legend.justification.inside     : NULL
 $ legend.location                 : NULL
 $ legend.box                      : NULL
 $ legend.box.just                 : NULL
 $ legend.box.margin               : 'margin' num [1:4] 0cm 0cm 0cm 0cm
  ..- attr(*, "unit")= int 1
 $ legend.box.background           : list()
  ..- attr(*, "class")= chr [1:2] "element_blank" "element"
 $ legend.box.spacing              : 'simpleUnit' num 11points
  ..- attr(*, "unit")= int 8
  [list output truncated]
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi TRUE
 - attr(*, "validate")= logi TRUE

Exercise 2

Using the perc_increase dataset derived from the tech_stocks dataset, recreate the following graphic as precisely as possible.

Hints:

  • Hex color code #56B4E9
  • Justification of 1.1
  • Size is 5
Code 
# percentage increase data
perc_increase <- tech_stocks |>
  arrange(desc(date)) |>
  distinct(company, .keep_all = TRUE) |>
  mutate(
    perc = 100 * (price - index_price) / index_price,
    label = str_c(round(perc), "%", sep = ""),
    company = fct_reorder(factor(company), perc)
  )
Solution
Code 
ggplot(perc_increase, aes(x = perc, y = reorder(company, perc))) +
  geom_bar(stat = "identity", fill = "#56B4E9") +
  geom_text(
    mapping = aes(label = label),
    color = "white",
    size = 5,
    hjust = 1.1) +
  scale_x_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme_minimal()

Exercise 3

Warning

Some thoughtful data wrangling will be needed and it will be demonstrated in class — Do not expect a video.

Examine the data and the plot provided in undergraduate-admissions-statistics.pdf — this pdf was collected from the Northwestern Data Book webpage. As you can see they have overlaid two plot types on one another by using dual y-axes.

There is one major error they make with the bars in their graphic. Explain what it is.

Solution

The error they did was stacking the bars. You can see in the undergrad bar graphs that the numbers don’t align with the application numbers. For example, for year 2000, there was a total of 14727 applications however exceeds over 20,000 applications on the y axis. The red bars and blue bars are supposed to be included in the green bars, and they should’ve done side by side plots, or a shading scheme, venn diagram, etc. All the bars should’ve started at point 0 and go up.

Using NU_admission_data.csv1, create two separate plots that display the same information instead of trying to put it all in one single plot — stack them using patchwork or cowplot.

  • 1 Data is taken from the pdf. The file includes a few extra years.

    • Which approach do you find communicates the information better, the single dual y-axes plot or the two separate plot approach? Why?

    Solution

    The separate plot approach is better because it doesn’t confuse admission application numbers with the admission and yield rate. It is harder to interpret where there are so much information on one plot, simplicity with one specific focus should be prioritized.

    Hints:

    • Form 4 datasets (helps you get organized, but not entirely necessary):
      • 1 that has bar chart data,
      • 1 that has bar chart label data,
      • 1 that has line chart data, and
      • 1 that has line chart labels
    • Consider using ggsave() to save the image with a fixed size so it is easier to pick font sizes.
    Solution
    Code 
    #data wrangling (move to tidy format for data)
bar_data <- admin_data |> 
  filter(year > 1999) |> 
  select(-contains("_rate")) |> 
  pivot_longer(
    cols = -year, 
    names_to = "category",
    values_to = "value"
  ) |> 
  mutate(
    bar_label = prettyNum(value, big.mark = ",")
  )

#bar plot 
plot_1_bar <- ggplot(data = bar_data, 
       mapping = aes(x = year, y = value, fill = category)
       ) +
  geom_col(position = "identity", width = 0.75) +
  geom_text(
    mapping = aes(label = bar_label),
    color = "white",
    size = 2,
    vjust = 1,
    nudge_y = -300) +
  scale_x_continuous(
    name = "Entering Year",
    breaks = 2000:2020, #seq(2000,2020),
    expand = c(0,0.25)
) + 
  scale_y_continuous(
    name = "Applications",
    limits = c(0,50000),
    breaks = seq(0,50000,5000),
    labels = scales::label_comma(),
    expand = c(0,0)
    )+
  theme_classic() +
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5, 1),
    legend.direction = "horizontal" ,
    plot.title = element_text(hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  ) +
  scale_fill_manual(
    name = NULL, 
    limits = c("matriculants", "admitted_students", "applications"),
    labels = c("Matriculants", "Admitted_students", "Applications"),
    values = c("#4E2A84","#836EAA", "#B6ACD1")
  ) +
  labs(
    title = "Northwestern University",
    subtitle = "Undergraduate Admissions 2000-2020"
  )
  
#display bar plot
plot_1_bar

    Code 
    #data wrangling (move to tidy format for data)
rate_data <- admin_data |> 
  filter(year > 1999) |> 
  select(year, contains("_rate")) |> 
  pivot_longer(
    cols = -year, 
    names_to = "rate_type",
    values_to = "value"
  )  |> 
 mutate(
   rate_label = str_c(value, "%"),
   label_y = case_when(
   rate_type == "yield_rate" ~ value + 2,
   rate_type == "admission_rate" ~ value + 2,
   )
 )

#line chart(rate plot)
plot_2_rate <- ggplot(data = rate_data, 
       mapping = aes(x = year, y = value, color = rate_type, shape = rate_type)
       ) +
  geom_line() +
  geom_point(mapping = aes(fill = rate_type), color = "white", size = 3, stroke = 1) +
  geom_text(
    mapping = aes(y = label_y, label = rate_label),
    size = 2,
    show.legend = FALSE) +
  scale_x_continuous(
    name = "Entering Year",
    breaks = 2000:2020, #seq(2000,2020),
    expand = c(0,0.25)
) + 
  scale_y_continuous(
    name = "Rate",
    limits = c(0,60),
    labels = scales::label_percent(scale = 1),
    expand = c(0,0),
    position = "right"
    )+
   scale_color_discrete(
    name = NULL, 
    limits = c("admission_rate", "yield_rate"),
    labels = c("Admission_rate", "Yield_rate")
  ) +
  scale_fill_discrete(
    name = NULL, 
    limits = c("admission_rate", "yield_rate"),
    labels = c("Admission_rate", "Yield_rate")
  ) +
  scale_shape_manual(
    name = NULL, 
    limits = c("admission_rate", "yield_rate"),
    labels = c("Admission_rate", "Yield_rate"),
    values = c(21, 24)
  ) +
  theme_classic() +
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5, 1),
    legend.direction = "horizontal" ,
    plot.title = element_text(hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  ) +
  labs(
    title = "Northwestern University",
    subtitle = "Undergraduate Admissions 2000-2020"
  )

#display plot
plot_2_rate

    Code 
    plot_2_rate / plot_1_bar